Burden and determinants of puerperal sepsis in Ethiopia: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Puerperal sepsis is a leading cause of maternal morbidity and is responsible for nearly one-fifth of maternal deaths worldwide. In Ethiopia, it remains a significant contributor to maternal mortality; however, a comprehensive understanding of its determinants is still lacking. Therefore, the aim of this systematic review and meta-analysis is to estimate the pooled prevalence of puerperal sepsis and to identify its determinant factors in Ethiopia, thereby providing comprehensive and up-to-date evidence to inform maternal health interventions. METHODS: statistic. A funnel plot and Egger's test were used to assess publication bias. Pooled prevalence of puerperal sepsis and effect sizes of determinant factors were assessed with 95% C. I. The pooled estimates were presented using a random effect model. RESULTS: This study involved 5247 postpartum women in Ethiopia. The prevalence of puerperal sepsis was 15.82% (95%CI 9.84-21.80). Determinant factors of puerperal sepsis were cesarean section deliveries AOR 2.86(95%CI 2.17-3.76), more than five repeated vaginal examinations AOR 4.91(95%CI 3.84-6.28), low number of antenatal care contact AOR 5.52 (95%CI 3.63-8.40), prolonged labor AOR 5.56(95%CI 4.14-7.47), premature rupture of membranes AOR 3.86(95%CI 3.00-4.96), rural residence AOR 5.15(95%CI 3.99-6.65),and home deliveries AOR 3.45(95%CI 2.28-5.24). CONCLUSIONS: The prevalence of puerperal sepsis in Ethiopia is high. Cesarean delivery, more than five vaginal examinations, lack of antenatal care, prolonged labor, premature rupture of membranes, rural residence, home delivery, and gestational diabetes were identified as significant risk factors for puerperal sepsis in Ethiopia. Therefore, adhering to labor care guide, and infection prevention protocol are paramount.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.033 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".